Next-Gen Manufacturing: Machine Learning for Surface Roughness Prediction in Ti-6Al-4V Biocompatible Alloy Machining

Abstract

Mechanical engineering plays an important role in the design and manufacture of medical devices, implants, prostheses, and other medical equipment, where the machining of bio-compatible materials have a special place. There are a lot of different conventional and non-conventional types of machining of biocompatible materials. One of the most frequently used methods is milling. The first part of this research explores the machining parameters optimization minimizing surface roughness in milling titanium alloy Ti-6Al-4V. A full factorial design involving four factors (cutting speed, feed rate, depth of cut, and the cooling/lubricating method), each having three levels, implies the 81 experimental runs. Using the Taguchi method, the number of experimental runs was reduced from 81 to 27 through an orthogonal design. According to the analysis of variance (ANOVA), the most significant parameter for surface roughness is feed rate. The second part explores the possibilities of using different ML techniques to create a predictive model for average surface roughness using the previously created small datasets. The paper presents a comparative analysis of several commonly used techniques for handling small datasets and regression problems. The best results indicate that the widely used machine learning algorithm Random Forest excels in handling regression problems and small datasets.

Analysis of Cutting Forces and Geometric Surface Structures in the Milling of NiTi Alloy

“…By capturing the interactions of all factors and test outcomes, a polynomial equation is established that precisely characterizes the relationship between the factors and the response values. Extracting optimal parameter values from the response surface aids in effectively minimizing research costs while ensuring optimal outcomes [9,25].…”

“…It is worth noting that, compared to titanium alloy, nickel alloy, maraging steel, etc., machining a NiTi alloy may present even greater challenges due to the unique properties of the alloy [5,13,24]. These distinctive characteristics necessitate specialized machining approaches and tooling strategies to address the inherent challenges and optimize the machining of NiTi alloys [13,25]. However, to uphold the importance of NiTi alloys as construction materials, efforts should be directed towards reducing their processing costs (due to their challenging machinability).…”

“…Historically, the industry's priority has been obtaining high-quality parameters for products made of the NiTi alloy, with production cost as a secondary concern. It can be anticipated that, in the future, due to the need to reduce and optimize production costs, research efforts will be focused on shaping NiTi alloy elements using methods that ensure maximum efficiency while maintaining the quality of the machined surface [19,21,[23][24][25][26].…”